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Improving the performance of adaptive arrays in nonstationary environments through data-adaptive training

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2 Author(s)
D. J. Rabideau ; Lincoln Lab., MIT, Lexington, MA, USA ; A. O. Steinhardt

Adaptive array algorithms based on sample matrix inversion require the availability of a secondary data set to "train" the adaptive filter. Numerous data-independent rules have been proposed for selecting this training data. However, such rules often perform poorly in highly nonstationary environments. In this paper, we present data-adaptive techniques for selecting the training data. The techniques, called power selected training and power selected de-emphasis, use measurements of the interference environment to select training data. This paper describes the algorithms, as well as optimality, complexity, and performance on recorded radar data.

Published in:

Signals, Systems and Computers, 1996. Conference Record of the Thirtieth Asilomar Conference on  (Volume:1 )

Date of Conference:

3-6 Nov. 1996